Hybrid ensemble-based machine learning model for predicting phosphorus concentrations in hydroponic solution

machine learning techniques can improve accuracy for predicting phosphorus without using labels, despite requiring longer computational time. potential solution to address soil shortages by 2050. Hywhere nutrient solutions of nutrients consisting of Phosphorus is an essential macro development in hydroponic crops, respiration, cell division, root formation. Measuring individual determining the correct amount of dimensional spectroscopy datasets. This study aims to compare the performance of single and hybrid machine learning models in predicting phosphorus concentrations using spectroscopy datasets. The Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Random Forest (RF) are utilized as the base for developing the hybrid model. To the best of our knowledge, no studies have compared the performance of single and hybrid machine learning models in predicting phosphorus concentrations using spectroscopy datasets. 2. Materials and methods